On-policy distillation (OPD) provides teacher supervision on student-generated trajectories, but a single trajectory realizes only one continuation at each decision, limiting supervision coverage under a finite sampling budget. We propose BranchOPD (Branching On-Policy Distillation), which selects informative decision points using student uncertainty and teacher-student disagreement, resamples student-policy continuations from shared prefixes, and applies token-level teacher supervision to both main trajectories and branches. BranchOPD operates at token-level decisions in mathematics and within-turn decisions in agent tasks. Shared-prefix reuse reduces student-generated token costs by approximately 19% on ALFWorld and 31% on WebShop relative to seven independent trajectories. We also introduce protocol-boundary repair (PBR), which uses targeted supervision under protocol constraints to mitigate repetition and wasted generation. BranchOPD outperforms Vanilla OPD and Multi-Rollout OPD (n=7) across all evaluated benchmarks, with gains over Vanilla OPD of 18.55 percentage points in mean mathematics accuracy over 16 sampled answers (avg@16) and 13.8 points in mean ALFWorld ID/OOD success. Mechanistic analyses and selection ablations show that branching at uncertain decisions expands subsequent behavior coverage and outperforms random selection and turn-initial branching.
Calibrating Teacher--Student Discrepancy for On-Policy Distillation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；College of Software Engineering, Southeast University（东南大学软件工程学院）；Nanjing University of Posts and Telecommunications（南京邮电大学）
SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Agency for Science, Technology and Research (A*STAR), Singapore（新加坡科技研究局）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Rice University（莱斯大学）
Reasoning Compression with Mixed-Policy Distillation
Technical University of Munich（慕尼黑技术大学）；GESIS – Leibniz Institute for the Social Sciences（莱布尼茨社会科学研究所）；Northeastern University（东北大学）；LMU Munich（慕尼黑大学）；Siemens AG（西门子股份公司）；University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Mina AI
Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization
Tianjin University（天津大学）；Beijing Institute of Technology（北京理工大学）；Singapore Management University（新加坡国立大学）；University of Chinese Academy of Sciences（中国科学院大学）；Xiaomi Inc（小米公司）
Step-Level On-Policy Distillation: Interpolating Between On-Policy Distillation and Supervised Fine-Tuning
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件技术国家重点实验室）；XingYun Lab, HUJING Digital Media & Entertainment Group（星云实验室，沪景数字媒体娱乐集团）；University of Chinese Academy of Sciences（中国科学院大学）；School of Data Science, Fudan University（复旦大学数据科学学院）
Condition-Contrastive Extrapolation for On-Policy Distillation of Language Models
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来智能网络研究院）；ByteDance（字节跳动）
Department of Computer Science（计算机科学系）；Engineering, Shanghai Jiao Tong University（上海交通大学工程学院）；Shanghai Innovation Institute（上海创新研究院）；Large Language Department, Tencent（腾讯大语言部门）
Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
School of Computer Science and Engineering, Northeastern University, China（东北大学计算机科学与工程学院）；Tsinghua University（清华大学）；Meituan（美团）；Meta AI；NiuTrans Research, Shenyang, China（新译研究院，沈阳，中国）
ResOPD: Tail Residualization for Sparse On-Policy Distillation
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Fudan University（复旦大学）
Institute of Trustworthy Embodied AI（可信具身人工智能研究院）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Ant Group（蚂蚁集团）；Zhejiang University（浙江大学）；City University of Hong Kong（香港城市大学）
